Risk Assessment System For Assessing Current Driver Behavior Relative to Past Behavior and Behaviors of Other Drivers

ABSTRACT

A risk assessment system is provided and includes a behavior description generator, driving behavior comparator, and risk assessment modules and a transceiver. The behavior description generator module receives a set of driving features and topics corresponding to a driving behavior of a first driver in a location and under a set of driving conditions. The driving behavior comparator module: compares the set of driving features and the topics to other sets of features and topics, which correspond to driving behaviors of other drivers for the location and same or similar driving conditions as the set of driving conditions; and generates an anomaly score for the first driver based on results of the comparison. The risk assessment module calculates a risk assessment score for the driver based on the anomaly score, where the risk assessment score is indicative of a risk level of the first driver relative to the other drivers.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure is related to U.S. patent application Ser. No.14/731,766 filed on Jun. 5, 2015, now U.S. Pat. No. 9,527,384. Theentire disclosure of the application referenced above is incorporatedherein by reference.

FIELD

The present disclosure relates to driver risk assessment systems.

BACKGROUND

The background description provided here is for the purpose of generallypresenting the context of the disclosure. Work of the presently namedinventors, to the extent it is described in this background section, aswell as aspects of the description that may not otherwise qualify asprior art at the time of filing, are neither expressly nor impliedlyadmitted as prior art against the present disclosure.

Traditional driver risk assessment systems that are used by insurancecompanies and fleet management companies compare driver parameters withpredetermined thresholds and assess driver risk based on thesecomparisons. Example parameters are vehicle speed and acceleration. Forexample, if a vehicle speed exceeds a predetermined threshold, a driverrisk assessment system may reduce an assessment score of thecorresponding driver. The assessment score may be provided to aninsurance company and/or a fleet management company. The insurancecompany may adjust an insurance rate of the driver based on theassessment score. The driver may be a taxi, track or bus driver for thefleet management company and the fleet management company may adjust apay rate of the driver based on the assessment score.

SUMMARY

A risk assessment system is provided and includes a behavior descriptiongenerator module, a driving behavior comparator module, a riskassessment module, and a transceiver. The behavior description generatormodule is configured to receive a first set of driving features andtopics corresponding to a driving behavior of a first driver of a firstvehicle in a location and under a first set of driving conditions. Thedriving behavior comparator module is configured to (i) compare thefirst set of driving features and the topics to one or more other setsof features and topics, wherein the one or more other sets of featuresand topics correspond to driving behaviors of other drivers for thelocation and same or similar driving conditions as the first set ofdriving conditions, and (ii) generate an anomaly score for the firstdriver based on results of the comparison. The risk assessment module isconfigured to calculate a risk assessment score for the driver based onthe anomaly score, wherein the risk assessment score is indicative of arisk level of the first driver relative to the other drivers. Thetransceiver is configured to transmit the risk assessment score from arisk assessment server to at least one of a vehicle control module ofthe first vehicle or a second server, where the risk assessment serverand the second server are separate from the first vehicle.

In other features, a risk assessment system is provided and includes adriving behavior comparator module, an assessment module, and atransceiver. The driving behavior comparator module is configured to (i)perform comparisons between sets of features and topics for drivingbehaviors of drivers, where each of the comparisons is for a samedriving location and same or similar driving conditions, and (ii)generate anomaly scores for the drivers based on results of thecomparison. The assessment module is configured to, based on the anomalyscores, determine at least one of a common driving behavior or a trendin the driving behaviors of the drivers for the driving locations andcorresponding ones of the driving conditions. The transceiver isconfigured to transmit information regarding the common driving behavioror the trend in the driving behaviors of the drivers to a target vehiclefor at least one of training a driver of the target vehicle orautonomous control of the target vehicle.

In other features, a system for a first vehicle is provided. The systemincludes sensors, a transceiver, and a vehicle control module. Thesensors and a global positioning system configured to determineparameters and driving conditions for a location of the first vehicle.The parameters indicate at least a current or upcoming location of thefirst vehicle. The transceiver is configured to transmit from the firstvehicle the parameters and driving conditions to a risk assessmentserver. The risk assessment server is separate from the vehicle. Thetransceiver is configured to, based on the parameters and drivingconditions, receive information regarding a common driving behavior formultiple drivers. The drivers do not include a first driver of the firstvehicle. The common driving behavior is indicative of an average drivingbehavior of the drivers for the current or upcoming location. Thevehicle control module is configured to, based on the informationregarding the common driving behavior, at least one of (i) train thefirst driver of the first vehicle, or (ii) autonomously controloperation of the first vehicle.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description, the claims and the drawings. Thedetailed description and specific examples are intended for purposes ofillustration only and are not intended to limit the scope of thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1 is a functional block diagram of an example of a driver riskassessment system including a risk assessment server in accordance withan embodiment of the present disclosure;

FIG. 2 is a functional block diagram of an example of a vehicleincorporating a feature reporting module in accordance with anembodiment of the present disclosure;

FIG. 3 is a functional block diagram of an example of a portion of therisk assessment system of FIG. 1 including servers in accordance with anembodiment of the present disclosure;

FIG. 4 is an example block diagram illustrating operation of a latentDirichlet allocation (LDA) module in accordance with an embodiment ofthe present disclosure;

FIG. 5 is a functional block diagram of an example of a behaviordescription generator module in accordance with an embodiment of thepresent disclosure;

FIG. 6 is a block diagram schematically illustrating an example of agraphical model hypothesized by LDA in accordance with an embodiment ofthe present disclosure;

FIG. 7 is a functional block diagram of an example of a vehicleincluding vehicle control modules in accordance with an embodiment ofthe present disclosure;

FIG. 8 illustrates an example vehicle reporting and responding method inaccordance with an embodiment of the present disclosure;

FIG. 9 illustrates an example risk assessment method in accordance withan embodiment of the present disclosure;

FIG. 10 illustrates an example driver specific scale adjusting method inaccordance with an embodiment of the present disclosure;

FIG. 11 is a plot of an example of probability distribution functionsfor multiple drivers;

FIG. 12 is a plot of an example of cumulative distribution functions formultiple drivers;

FIG. 13 is an example top view of vehicles illustrating an anomaly of avehicle at a particular location;

FIG. 14 is top view of example locations and plots of parameters for avehicle at the locations in accordance with an embodiment of the presentdisclosure;

FIG. 15 is a graphical view illustrating example topic proportions forsome of the scenes of FIG. 14;

FIG. 16 is a graphical view illustrating example topic proportions forindividual scenes;

FIG. 17 is a graphical view illustrating example distributions of thescenes of FIG. 16;

FIG. 18 is a graphical view illustrating probabilities and features ofone of the topics of the scenes of FIG. 16;

FIG. 19 is a graphical view illustrating example topic proportions andsimilar anomaly scores for different trips corresponding to the scenesof FIG. 16;

FIG. 20 is a graphical view illustrating example topic proportions andanomaly scores, including an outlier anomaly score, for different tripscorresponding to the scenes of FIG. 16;

FIG. 21 is an example top view of vehicles illustrating common lanetransitioning behavior at a particular location;

FIG. 22 is an example top view of vehicles illustrating common speedbehavior at particular locations; and

FIG. 23 is an example display reporting driving behavior information inaccordance with an embodiment of the present disclosure.

In the drawings, reference numbers may be reused to identify similarand/or identical elements.

DETAILED DESCRIPTION

Drivers that exhibit similar driving behavior as other drivers in a samelocation at a similar time of day and under similar driving conditionstend to be safer drivers than drivers that exhibit different drivingbehavior. This is because the drivers that exhibit similar drivingbehavior tend to be safer drivers, since their behavior is common andexpected. Traditional risk assessment systems do not consider drivingbehavior and trends of multiple drivers when evaluating risk of acertain driver. A driver risk assessment server is disclosed herein thatmonitors driving behaviors of drivers in various locations atcorresponding times of day and under corresponding conditions. Thedriving behaviors that are related due to being associated with asimilar location and conditions are compared to assess risk of eachdriver. This provides improved and more accurate risk assessment.

An insurance company may consider an amount of driving experience of adriver as an indicator of a skill level of that driver. A driver with ahigher skill level may pay less for insurance than a driver with a lowskill level. As a result, drivers that do not drive often, such asdrivers that utilize car sharing programs to minimize transportationscosts may be required to pay more for car insurance. This may be due toa perceived skill level of the driver corresponding to the driver's lowlevel of driving experience irrespective of how safe the actual drivingbehavior is of the driver. The disclosed driver risk assessment serverprovides risk assessment scores to indicate how safe the actual drivingbehavior is of a driver. This allows safer drivers to be identified,which allows insurance companies to more accurately set insurance ratesfor drivers. This also enables insurance companies to provide incentivesto riskier drivers to improve their driving habits and/or behavior andas a result become safer drivers.

Data science is the practice of applying correct models to correct datato provide insights. Data science is used in the following disclosedimplementation examples to describe and understand driving behavior andimprove operations of advanced driver assistance systems andafter-market services.

FIG. 1 shows a driver risk assessment system 10 that includes vehicles,a risk assessment server 12, a sharing server 14, an insurance server 16and one or more other servers 18. Although not shown, communicationbetween the servers 12, 14, 16, 18 and the vehicles 20, 22 may be viabase stations, satellites, gateways, switches, and/or other intermediarycommunication devices. The vehicles may include a target vehicle 20 andother vehicles 22. Any one of the vehicles 20, 22 may be a targetvehicle. A target vehicle refers to a vehicle including a driver forwhich driving behavior is being monitored and evaluated for riskassessment via the driver risk assessment server 12. The other serversmay include, for example, a fleet management server and/or other thirdparty servers. The vehicles may communicate various informationincluding parameters, features, etc. describing behavior of the vehiclesto the driver risk assessment server 12, which may then provide feedbackto the vehicles 20, 22 and/or provide risk assessment scores and/orother driving behavior related information to the servers 14, 16, 18.The feedback may include the driver behavior related information. Afeature may refer to, for example, a range (e.g., 60-70 km/hr) of aparameter (e.g., vehicle speed) or a certain behavior (e.g., braking,increasing speed, turning at a certain steering angle, etc.). Thevehicles 20, 22 may perform various tasks based on the feedbackincluding reporting the assessment scores to the drivers, alteringautonomous vehicle operation, training the drivers how to operate thevehicles 20, 22 in certain locations, etc.

The sharing server 14 may store: the information received from thevehicles 20, 22; the risk assessment scores; and/or other datapertaining to the vehicles 20, 22, the drivers of the vehicles 20, 22,locations through which the vehicles 20, 22 are driving, and/orconditions of the locations. The insurance server 16 may receive therisk assessment scores and adjust insurance rates for the drivers of thevehicles 20, 22. The other server(s) 18 may adjust pay rates and/orother benefits of the drivers based on the risk assessment scores.Operations of the servers 12, 14, 16, 18 and the vehicles 20, 22 arefurther described below.

FIG. 2 shows an example of one of the vehicles 20, 22 of FIG. 1. Thevehicle, designated 50, includes sensors 52, vehicle control modules 54,a global positioning system (GPS) receiver 56 and a feature reportingmodule 58. The feature reporting module 58 may be separate from thevehicle control modules 54, as shown, or may be implemented as one ofthe vehicle control modules 54. The feature reporting module 58 includesa driver-behavior data obtaining module 60, an environmental-dataobtaining module 62, a driving situation discretizer module 64, afeature distribution generator module 66 and a feature distributiontransceiver 68.

The sensors 52 may include a first type of sensors each measuring acurrent quantity or a current state of operation of a corresponding oneof driver-operable devices (e.g., a brake pedal, a gas pedal, a steeringwheel, a signal indicator, etc.) installed in the vehicle. The firsttype of sensors output time-series driving data sequences consisting ofsequentially or cyclically measured data indicative of how the operatedquantity or the operated condition of a corresponding one of thedriver-operable devices changes. For example, the first type of sensorsincludes an accelerator sensor, a brake sensor, a steering sensor,indicator sensors, a shift-position sensor and/or other sensors. Theaccelerator sensor provides an output indicative of a position of a gaspedal of the vehicle and/or an acceleration rate of the vehicle. Thebrake sensor provides an output indicative of a position of a brakepedal, an applied pressure on a brake pedal, and/or a percentage of atotal available amount of braking of the vehicle. The steering sensorprovides an output indicative of a steering angle of a steering wheel ofthe vehicle. The indicator sensors provide outputs indicative of, forexample, operation of turning signals on the vehicle. The shift positionsensor provides an output indicative of a driver-selectable position ofa transmission of the vehicle.

The sensors 52 also include a second type of sensors each measuring aparameter indicative of how a behavior of the vehicle changes andoutputting a time-series measurement data sequence. For example, thesecond type of sensors may include a vehicle speed sensor, a yaw-ratesensor, image sensors (e.g., cameras), ultrasonic sensors, radarsensors, LiDAR sensors, etc. The vehicle speed sensor provides an outputindicative of a speed of the vehicle. The yaw-rate sensor provides anoutput indicative of an angular velocity, i.e. a yaw rate around avertical axis of the vehicle. The image sensors may provide images of anenvironment around the vehicle 50. The radar sensors may measurepositions of objects located around the vehicle 50 and/or relativespeeds of the object relative to the vehicle. The second type of sensorsmay also include an interior measurement unit (IMU) that repeatedlymeasures 3-axis (i.e. three-dimensional angular velocities andacceleration values) using gyro sensors and/or accelerometers. Thesensors 52 may further be used to detect and/or determine a time zone, adate, weather conditions, and states of infrastructures located aroundthe vehicle.

The time-series measurement data sequences of the second type of sensorsrepresent how the behavior of the vehicle changes based on driveroperations of the driver-operable devices. The time-series measurementdata sequences output from the second type of sensors will be referredto as time-series behavior-data sequences.

The driving-behavior data obtaining module 60 receives the time-seriesdriving-data sequences and the time-series behavior-data sequences fromthe sensors 52. This may be via one or more of the vehicle controlmodules 54. The driving-behavior data obtaining module 60 differentiatesthe time-series driving-data sequences to generate first differentialdata sequences. Additionally, the driving-behavior data obtaining module60 differentiates the time-series behavior-data sequences to generatesecond differential data sequences. Then, the driving-behavior dataobtaining module 60 groups the time-series driving-data sequences, thetime-series behavior-data sequences, the first differential datasequences, and the second differential data sequences to generatemultidimensional time-series data sequences. The driving-behavior dataobtaining module 60 outputs, as driving behavioral data sequences, themultidimensional time-series data sequences to each of the modules 64,66.

The environmental-data obtaining module 62 may also receive outputs ofthe sensors. This may be via one or more of the vehicle control modules54, as shown. The environmental-data obtaining module 62 is connected tothe GPS receiver 56, which receives signals sent from GPS satellites andgenerates three-dimensional positional information based on the receivedsignals. The GPS receiver 56 outputs a time-series positional datasequence including the three-dimensional positional information. Theenvironmental-data obtaining module 62 may receive: the time-seriesimage data sequence; the time-series position-data sequence; thetime-series relative-speed data sequence; the time-series 3Dangular-velocity data sequence; the time-series acceleration-datasequence; the time-series time-zone data sequence; the time-seriesweather-data sequence; and the time-series infrastructural-datasequence. These data sequences represent driving environments of thevehicle. The environmental-data obtaining module 62 outputs the datasequences to the feature distribution generator module 66 asenvironmental data sequences.

The driving situation discretizer module 64 stores a driver model thatsimulates driving operations of drivers and recognition operations ofthe environments around each driver. The driving situation discretizermodule 64 statistically analyzes the driving behavioral data sequencesoutput from the driving-behavior data obtaining module 60. Thisstatistic analysis extracts each point of time where a common driverfeels a change from a current driving situation to another drivingsituation. The driving situations represent driving conditions andenvironments of a vehicle. For example, each of the driving situationsrepresents various items of information indicative of how the driverdrives a vehicle in certain environments.

According to the results of extraction, the driving situationdiscretizer module 64 discretizes the driving behavioral data sequencesinto segmented behavior-data sequences; where each of the segmentedbehavior-data sequences corresponds to one of the driving situations.The driving situation discretizer module 64 extracts sequence of timesections, each of which matching with a corresponding one of thesegmented behavior-data sequences. This is further described in U.S.Pat. No. 9,527,384, which is incorporated herein by reference.

The feature distribution generator module 66 generates a distribution offeature quantities included in each of the segmented behavior-datasequences experienced in each of the driving situations. Similarly, thefeature distribution generator module 66 generates a distribution offeature quantities experienced in each of the environmental datasequences output from the environmental-data obtaining module 62 foreach of the driving situations. For example, each of the segmentedbehavior-data sequences may include: a first feature quantity sequenceFQ1, which is indicative of a position of a driver-operable gas pedal; asecond feature quantity sequence FQ2, which is indicative of a positionof a driver-operable brake pedal; a third feature quantity sequence FQ3,which is indicative of a position of a steering wheel; a fourth featurequantity sequence FQ4, which is indicative of a velocity of the vehicle;fifth to seventh feature quantity sequences FQ5 to FQ7, each of which isthe sequence of differential quantities of a corresponding one of thefirst to third feature quantity sequences FQ1 to FQ3; and an eighthfeature quantity sequence FQ8, which is the sequence of differentialquantities of the fourth feature quantity sequence FQ4. Each of thesegmented behavior-data sequences may include sequences for the otherparameters and/or features disclosed herein. Each of the first to eighthfeature quantity sequences FQ1 to FQ8 has a corresponding range with lowand high limits.

The feature distribution generator module 66 uses the ranges of thesegmented behavior-data sequences as a corresponding feature space, andgenerates a distribution for each feature quantity in a correspondingone of the feature spaces. The feature distribution generator module 66also generates a distribution of feature quantities included in each ofthe environmental data sequences for each of the driving situations. Thefeature distribution generator module 66 divides the measured imagesconstituting the time-series image data sequence for each of the drivingsituations, thus obtaining segmented images corresponding to each of thedriving situations.

The feature distribution generator module 66 uses, as target featurequantities, scale-invariant feature transform (SIFT) quantities includedin the segmented images for each of the driving situations. The featuredistribution generator module 66 obtains SIFT quantities in each localregion in each of compressed image as a feature quantity. The featuredistribution generator module 66 uses the range of the SIFT quantitiesas a feature space and generates a distribution of the SIFT quantitiesin the feature space. The feature distribution generator module 66generates a bar for each of predetermined bins; the height of the barfor each of the predetermined bins represents the frequency ofcorresponding some of the SIFT quantities. This generates a distributionof the SIFT quantities as a histogram. The predetermined bins arepreferably determined based on learning. The feature distributiongenerator module 66 can use other feature quantities in place of theSIFT quantities. Accordingly, these operations of the featuredistribution generator module 66 generate a group of feature-quantitydistributions for each of the driving situations; the group of thefeature-quantity distributions for each of the driving situationsincludes: a first type of feature-quantity distributions based on thesegmented behavior-data sequences for a corresponding one of the drivingsituations; and a second type of feature-quantity distributions based onthe time-series environmental data sequences for a corresponding one ofthe driving situations. This is further described in U.S. Pat. No.9,527,384, which is incorporated herein by reference.

As an example, the number of the first and second types offeature-quantity distributions included in each of the drivingsituations may be set to n, where n is an integer equal to or more than2. The n feature-quantity distributions included in each of the drivingsituations can be referred to as the first feature-quantity distribution(first distribution) 1, . . . , to the n-th feature-quantitydistribution (n-th distribution) n.

First, the modules 60, 62, 64, 66 are operating while the vehicle istravelling. These operations generate learning first to n-thfeature-quantity distributions for each of the driving situations; thelearning first to n-th feature-quantity distributions for each of thedriving situations are correlated with corresponding learning images fora corresponding one of the driving situations. The feature distributiontransceiver 68 transmits the group of feature-quantity distributions tothe driver risk assessment server 10 of FIG. 1.

FIG. 3 shows a portion 100 of the driver risk assessment system 10 ofFIG. 1. The portion 100 includes the driver risk assessment server 12,the sharing server 14, the insurance server 16 and a target vehicle ormobile device 102. The driver risk assessment server 12 includes afeature distribution receiver 104, a LDA module 106, a behaviordescription generator module 108, a memory 110, a behavior comparatormodule 112, a risk assessment module 114, and a score transceiver 116.The modules 106, 108, 112, 114 may be implemented by one or moreprocessors.

The feature distribution receiver 104 receives groups offeature-quantity distributions from the feature distributiontransceivers of the vehicles 20, 22. The LDA module 106 determines,based on the groups of feature-quantity distributions, and/or otherparameters and/or features received from and/or associated with thevehicles 20, 22, topics. Each of the topics refers to a related group offeatures that together have a high probability of occurring.

FIG. 4 shows a LDA module 150 that receives as inputs feature datagrouped by scene and a number of topics to be created. A scene may referto a location, a topic, and a corresponding distribution of topicproportions for that location. Topic proportions refer to the percentageof drivers having a particular behavior that matches the features of oneor more topics.

In the example of FIG. 4, the LDA module 150 generates 10 topics. Thefeature data received by the LDA module may include steering, brake,throttle, and speed data for each of the scenes and for multiplevehicles. A top may be created for features that have a high probability(e.g., greater than a predetermined percentage) of occurring for ascene. As an example, example probabilities and features are shown basedon which topic 5 is created. As shown, speeds of 63 and 70kilometers/hour (km/hr) and throttle positions of 5, 10 and 15% havehigh probabilities of occurring. The LDA module 150 creates the 5^(th)topic to be 60-70 km/hr and throttle position of 5-15%. In a similarmanner the LDA module 150 creates the other topics 1-4 and 6-10. The LDAmodule 150 is further described below with respect to FIG. 6.

Referring now to FIGS. 3 and 5, which show the behavior descriptiongenerator module 108 that includes a memory 160, a topic proportioncalculation module 162, and a feature-portion information generatormodule 164. The memory 160 stores a driving-topic database 166 and atopic-description database 168.

The driving-topic database 166 has stored therein information indicativeof the topics. Each of the topics represents a corresponding drivingsituation that occurs while a vehicle is travelling. Each of the topicsis composed of n base feature-quantity distributions, where n the sameas the number n of the n feature-quantity distributions included in eachof the driving situations described above. The n base feature-quantitydistributions can be referred to as the first base feature-quantitydistribution (first base distribution) 1, . . . , and the n-th basefeature-quantity distribution (n-th base distribution) n. Theinformation of the topics is used to express the group of the first ton-th feature-quantity distributions for each of the driving conditionsas a combination of at least some of the topics.

The topics may be generated using a latent topic model used in the fieldof natural language processing where: each of the segmentedbehavior-data sequences, which corresponds to one of the drivingsituations, is regarded as one sentence; and measured, i.e. observedfeature quantities are regarded as individual words. Each of the topicsis based on multiple modalities, i.e. the driving behavioral datasequences and the environmental data sequences. In order to estimate thetopics using the n base feature quantity distributions, multimodal LDAcan be used as described in U.S. Pat. No. 9,527,384. LDA hypothesizes agraphical model for example as illustrated in FIG. 6. LDA is analgorithm associated with natural language processing. The algorithmtakes a set of observations, such as driving scenes, and generates a setof topics to describe the scenes. LDA is an unsupervised generativestatistical model. Reference character D represents the number of allthe drive situations. Reference character M_(d) represents the number ofimage frames included in a d-th driving situation. Reference character dis an identifier of a driving situation in the D driving situations.Reference character K represents the number of topics. Referencecharacter w_(d,m) represents a feature quantity observed, i.e. measured,in an m-th frame of the d-th driving situation. Reference character mrepresents an identifier of a frame in the M_(d) frames. Referencecharacter z_(d,m) represents a topic indicator variable indicative of atopic allocated for the feature quantity w_(d,m). The topic indicatorvariable z_(d,m) takes one of natural numbers 1 to K. Referencecharacter θ_(d) represents a multinomial distribution parameter θindicative of the ratio of respective topics included in the d-thsituation thereamong, and ϕ_(k) represents a multinomial distributionparameter ϕ indicative of the ratio of respective feature quantitiesgenerated from a k-th topic thereamong. Reference character k representsan identifier of a topic in the K topics. Reference characters α and βrepresent parameters for the respective multinomial distributionparameters θ_(d) and ϕ_(k).

The multinomial distribution parameter θ_(d), the multinomialdistribution parameter ϕ_(k), the topic indicator variable z_(d,m), andthe feature quantity w_(d,m) are respectively defined by the followingequations (1) to (4), where Dir represents a Dirichlet distribution, andMulti represents a multinomial distribution:

θ_(d)˜Dir(θ;α)  (1)

ϕ_(k)·Dir(ϕ;β)  (2)

z _(d,m)˜Mult(z;θ _(d))  (3)

w _(d,m)˜Multi(w;ϕ _(z) _(d,m) )  (4)

Using the model illustrated in FIG. 3 in accordance with a knownexpectation-maximization (EM) algorithm enables the multinomialdistribution parameters θ and ϕ to be estimated; the EM algorithmalternates between performing an expectation (E) step, which creates afunction for the expectation of the log-likelihood evaluated using thecurrent estimate for the parameters, and a maximization (M) step, whichcomputes parameters maximizing the expected log-likelihood found on theE step. In particular, the topics according to this embodiment are forexample generated based on many feature-quantity distributions forlearning previously generated.

The multimodal LDA is executed using the generated learning first ton-th feature-quantity distributions for each of the driving situationsto enable first to nth topics, each of which including first to n-thbase feature quantity distributions that are estimated. As describedabove, each of the topics based on a corresponding group of the first ton-th base feature quantity distributions set forth above represents acorresponding specific driving situation that latently, i.e.potentially, exists in corresponding driving behavioral data sequencesand environmental data sequences, and that frequently appears while adriver is driving the vehicle.

The topic proportion calculation module 162 selects some topics from thetopics stored in the driving-topic database 166 for each of the drivingsituations. The topic proportion calculation module 162 calculates thepercentages of each the selected topics with respect to all of theselected topics such that the calculated percentages of the selectedtopics most suitably expresses the first to n-th feature-quantitydistributions included in a corresponding one of the driving situations.

Specifically, the topic proportion calculation module 162 executes onlyone or more E steps included in the EM algorithm, thus calculating atopic proportion for each of the driving situations. The topicproportion for each of the driving situations represents the percentagesof selected topics to the whole of the selected topics for the first ton-th feature-quantity distributions included in a corresponding one ofthe driving situations.

For example, a topic proportion θ_(d) for a d-th driving situation canbe expressed by the following equation (5), where TP_(k) represents ak-th topic TP_(k) included in a topic proportion θ_(d), k represents anidentifier of a topic in K topics TP₁ to TP_(K), and θ_(d,k) representsa content ratio of the k-th topic TP_(k) in the topic proportion θ_(d).

θ_(d)=(θ_(d,1),θ_(d,2),θ_(d,3), . . . ,θ_(d,K))  (5)

That is, the topic proportion calculation module 162 calculates thesequence of topic proportions θ_(d) of the respective drivingsituations.

The topic-description database 168 has stored therein driver behavioraldescriptions defined to correlate with the respective topics. That is, atypical driving behavior of a driver for each topic is described.

Driver behavioral descriptions for the topics are then generated. First,a driving-topic generation method according to this embodiment uses thefirst type of feature-quantity distributions included in the first ton-th base feature-quantity distributions constituting each of the topicsin order to generate the driver behavioral descriptions for each of thetopics. Then, the driving-topic generation method extracts, from thefirst type of feature-quantity distributions, feature quantities thatare specific to each of the topics using a likelihood ratio L_(i,k),thus generating driver behavioral descriptions based on the results ofthe extraction for each of the topics.

The driving-topic generation method calculates the likelihood ratioL_(i,k) as a driving behavioral feature in accordance with the followingequation (6), where: w_(i) represents feature quantities belonging to ani-th bin; i represents an identifier of a bin included in all the bins;TP_(k) represents a k-th driving topic identified by k; ϕ_(k) representsa multinomial distribution indicative of the base feature-quantitydistributions of the k-th driving topic TP_(k); p(w_(i)|ϕ_(k))represents the conditional probability of the feature quantities w_(i)given multinomial distribution ϕ_(k) of the k-th driving topic TP_(k);

$\sum\limits_{k}{p( w_{i} \middle| \varphi_{k} )}$

represents a sum of all the conditional probabilities p(w_(i)|ϕ_(k))obtained for all the driving topics.

$\begin{matrix}{L_{i,k} = \frac{p( w_{i} \middle| \varphi_{k} )}{\sum\limits_{k}{p( w_{i} \middle| \varphi_{k} )}}} & (6)\end{matrix}$

The likelihood ratio L_(i,k) defined in the equation (6) is an indicatorcalled an inverse document frequency (IDF), which represents thefrequency or the probability of feature quantities, which belong to ani-th bin included in only a specific topic TP_(k).

The driving-topic generation method extracts primitive behavioralexpressions for each of the topics from a predetermined primitivebehavioral expression list for each of the topics in accordance withgraphs. Each of the graphs shows, for each of the topics, a distributionof the corresponding likelihood ratios L_(i,k) at the respective bins.Then, the driving-topic generation method joins the extracted primitivebehavioral expressions for each of the topics appropriately, thusgenerating a driver behavioral description for each of the topics. Thesedriver behavioral descriptions for the respective topics are stored inthe topic description-database 168 to correlate with the respectivetopics.

The feature-portion information generator module 164 performs afeature-portion information generation routine. The feature-portioninformation generation routine detects a feature portion in the sequenceof the driving situations when there is a significant change of a targetdriving situation from a latest previous driving situation. Then, thefeature-portion information generation routine extracts, from thefeature portion, a first driving topic Tpa having a correspondingpercentage in the topic proportion for a target driving situation thatmost significantly changes in percentage of all the topics. Thereafter,the feature-portion information generation routine sets a first topicdescription defined for the first topic Tpa as the first behavioraldescription D1.

The feature-portion information generation routine also obtains anaverage topic proportion π based on all driving situations belonging toa stable range defined as a range from a last previous detection time ofthe feature portion to a current detection time of the feature portion.Then, the feature-portion information generation routine extracts, fromthe average topic proportion π, a second driving topic Tpb that has amaximum percentage if the maximum percentage is greater than thepredetermined threshold. Thereafter, the feature-portion informationgeneration routine sets a second topic description defined for thesecond topic Tpa as the second behavioral description D2.

Following the setting of the first behavioral description D1 and/orsecond behavioral description D2, the feature-portion informationgeneration routine stores feature-portion information including thefirst behavioral description D1 and/or second behavioral description D2,the detected time of the corresponding feature portion, and astable-range length W. feature-portion information generator module 164may store the behavioral descriptions, feature proportions, andcorresponding behavioral trend and location information in the drivingbehavior trend and location database 170 in the memory 110. The trendinformation may indicate trends in changes of behavior of drivers forcorresponding locations.

The memory 110 may also store a driver risk assessment (DRA) driverhistory database (DB) 172, a DRA driver information DB 174, a DRAvehicle DB 176, a DRA vehicle history DB 178, a location DB 180 and aweather DB 182. The DRA driver history DB 172 stores features ofbehaviors of drivers for locations and conditions. The conditions mayrefer to, for example, weather conditions, traffic conditions, roadconditions, etc. The DRA driver information DB 174 stores driverinformation pertaining to each of the drivers, such as a driveridentifier (ID), an address, personal information (e.g., age, sex, andphone number), etc. The DRA vehicle DB 176 includes vehicle information,such as a vehicle serial number, a vehicle make, model and type, etc.The DRA vehicle history DB 178 stores vehicle history data, such assteering angles, throttle positions, brake percentages, accelerationrates, and/or other features for locations and conditions. The locationdatabase 180 stores location information, such as type of road, whetherthe location includes a turn, an on ramp and/or an off ramp, inclinationof the road, speed limit, etc. The weather database 182 includes weatherinformation for the locations, such as temperature, percent change ofprecipitation, average wind speed, etc. The sharing server 14 includes amemory 190 that stores databases, which may correspond to the databases172, 174, 176, 178, as shown and/or other databases. The memory 190 asshown stores a car-sharing (CS) driver history DB 192, a CS driverinformation DB 194, a CS vehicle DB 196, and a CS vehicle history DB198. The databases 192, 194, 196, 198 store similar information asstored in the databases 172, 174, 176, 178. The sharing server 14 mayshare the information stored in the databases 192, 194, 196, 198 withthe DRA server 12 and with other servers, such the servers 16, 18 ofFIG. 1.

The driving behavior comparator module 112 compares behavior of a driverof a target vehicle with behaviors of drivers of other vehicles togenerate anomaly scores. This may be based on the information, includingthe topic proportions and feature-proportion information, stored in thedriving behavior trend and location database 170 by the modules 162,164. An anomaly, in general, is an action that deviates from what isnormal or expected. FIG. 13 shows an example of a driving anomaly, wherea vehicle 199 performs a strong braking operation, turns sharply andcrosses two lanes of traffic to exit a freeway. FIG. 13 shows a path 201of the vehicle 199 relative paths 203 of other vehicles. The anomalyscores generated by the driving behavior comparator module 112 indicatewhether the driver of the target vehicle is behaving (i) similar ordifferent as the driver has behaved in the past and/or (ii) similar ordifferent than other drivers, when driving in a same location and undersimilar driving conditions, such as traffic conditions, weatherconditions, road conditions, etc.

The risk assessment module 114, based on the anomaly scores generates arisk assessment score. The risk assessment score is indicative ofwhether the driver is driving (i) safer or more dangerously than thedriver has in the past, and/or (ii) safer or more dangerously than otherdrivers, when driving in a same location and under similar drivingconditions. The score transceiver 116 transmits the risk assessmentscore to, for example, the insurance server 16 as shown and/or to theother server(s) 18, the target vehicle (or mobile device) 102, and/orother server and/or device. The mobile device may be, for example, acomputer, a cellular phone, a tablet, a wearable device and/or otherelectronic device of the driver. The target vehicle and/or mobile device102 may signal to the driver the risk assessment score.

In the example embodiment shown, the risk assessment score istransmitted to the insurance server 16. The insurance server 16 mayinclude a score transceiver 200, a pricing database 202, an insurancepremium calculation module 204 and an insurance cost transceiver 206.The score transceiver 200 receives the risk assessment score from thescore transceiver 116. The insurance premium calculation module 204calculates an insurance premium based on the risk assessment score. Thismay include looking up an insurance premium in the pricing database 202based on the risk assessment score. The insurance cost transceivertransmits the updated insurance premium to the target vehicle and/ormobile device to indicate to the driver the updated premium.

FIG. 7 shows a vehicle 300 including sensors 302, a GPS receiver 304,vehicle control modules 306, an engine 308, a converter/generator 310,and a transmission 312. The vehicle 300 may be implemented as one of thevehicles 20, 22 of FIG. 1. The sensors 302 detect environmentalconditions and status of vehicle devices. The sensors 302 may includethe sensors 52 of FIG. 2. The GPS receiver 304 may operate similar asthe GPS receiver 56 of FIG. 2.

The vehicle control modules 306 may include engine, converter/generator,transmission, brake, steering, electric motor control modules and/orother control modules that control operation of the engine 308, theconverter/generator 310, the transmission 312, turn signal indicators319, a brake system 320, one or more electric motor(s) 322, and steeringsystem 324. The engine 308, the converter/generator 310, thetransmission 312, the turn signal indicators 319, the brake system 320,the electric motor(s) 322, and the steering system 324 may includeactuators controlled by the vehicle control modules 306 to, for example,adjust fuel, spark, air flow, throttle position, pedal position, etc.This control may be based on the outputs of the sensors 302, the GPSreceiver 304, and feedback from the DRA server 12 of FIG. 3. Thefeedback may include a risk assessment score for a driver of the vehicle300. The vehicle control modules 306 may receive power from a powersource 330, which may be provided to the engine 308, theconverter/generator 310, the transmission 312, the turn signalindicators 319, the brake system 320, the electric motor(s) 322, thesteering system 324, etc. The other control modules of the vehiclecontrol modules 306 may include an autonomous control module 332, adriver training module 334, and a reporting module 338.

The autonomous control module 332 may control operation of the engine308, the converter/generator 310, the transmission 312, the turn signalindicators 319, a brake system 320, one or more electric motor(s) 322,and steering system 324 based on average and/or trend driver behaviorfor locations and conditions. Average and/or trend driver behaviorinformation may be provided by the DRA server 12 of FIG. 3 to thevehicle control modules 306. This may be done via transceivers of thevehicle 300 and the DRA server 12.

As an example, in a certain location and under a certain set of drivingconditions, a typical (or average) driver may drive at a certain speed,accelerate at a certain rate, exhibit a certain brake and/or steeringpattern, use a certain turn signal, set a certain cruising speed, etc.This and other average driver behavior information may be provided asdriving parameters and patterns to each of the vehicle control modules306, which may then perform tasks based on this information. The averagedriver behavior information may also include anomaly information and/oranomaly outliers to indicate where potential dangerous conditions exist.Anomaly information and anomaly outliers may be used to identifylocations where drivers on average are operating their vehicles moredangerously or differently than under the current driving conditions dueto a dangerous situation (e.g., a vehicle accident, an icy road, a downtree or powerline, an inanimate or animate object in a lane of traffic,etc.). An average driver may swerve a corresponding vehicle at a certainlocation to prevent an accident from occurring. This is an indication ofa dangerous location. As another example, the driver of the vehicle 300may operate the vehicle 300 more dangerously than other drivers at acertain location. As a result, the vehicle control modules 306 mayrespond to this by changing operation of the engine 308, theconverter/generator 310, the transmission 312, the turn signalindicators 319, the brake system 320, the electric motor(s) 322 and/orthe steering system 324 and/or by generating, for example, video and/oraudio signals via the display 340 and the audio system 350. This may bedone in an attempt to alter the behavior of the driver to be similar tothat of an average driver.

The autonomous control module 332 may, for example, control operation ofthe engine 308, the converter/generator 310, the transmission 312, abrake system 320, one or more electric motor(s) 322, and steering system324, such that the vehicle 300 exhibits these driving parameters and/orfollows the provided driving patterns for the current location of thevehicle 300 and driving conditions. This causes the vehicle to operateas a vehicle operated by an average driver for that location and drivingconditions. This control may also be based on the anomaly informationand/or anomaly outliers.

The driver training module 334 may indicate via a display 340 how tooperate the vehicle 300 to a driver to train the driver to act in asimilar manner as other typical drivers for that location and drivingconditions. The driver training module 334 may receive drivingparameters and/or patterns from the DRA server 12 and may then displaythese parameters and/or patterns on the display 340. For example, priorto entering a location and/or while in the location, the driver trainingmodule 334 may display steering indicators, braking indicators, gaspedal indicators, turn signal indicators, driving path indicators, etc.to a driver to influence the driver to act in a certain manner for thecurrent location of the vehicle 300 and driving conditions. The actionsperformed by the driver training module 334 may also be based on theanomaly information and/or anomaly outliers. The driver training module334 generator indicators via the display 340 and/or the audio system 350to indicate to the driver that the driver is driving dangerously and/orappears to be driving impaired and provide indicators to aid the driverto drive more safely. One of the modules 332, 334 may performcountermeasures to avoid an accident by controlling operations of thevehicle 300 should the driver be impaired. This may include limitingspeed of the vehicle, reducing speed of the vehicle, maintainingpredetermined distances from objects, etc.

The reporting module 338 may display a risk assessment score of thedriver as received from the DRA server 12, an updated insurance rate, anupdated pay rate, and/or other related information. This may be done viathe display 340.

The systems disclosed herein may be operated using numerous methods,example methods are illustrated in FIGS. 8-10. In FIG. 8, an examplevehicle reporting and responding method is shown. Although the followingmethods are shown as separate methods, one or more methods and/oroperations from the separate methods may be combined and performed as asingle method. Although the following operations of the followingmethods are primarily described with respect to the implementations ofFIGS. 1-10, the operations may be easily modified to apply to otherimplementations of the present disclosure. The operations may beiteratively performed.

The method may begin at 400. At 402, the vehicle control modules 54, 306collect sensor data from the sensors 52, 302. At 404, the vehiclecontrol modules 54, 306 collect GPS data from the GPS receivers 56, 304.

At 406, the data obtaining modules 60, 62 determine driving-behaviordata and environmental data as described above with respect to FIG. 2.At 408, the driving situation discretizer module 64 analyzes the drivingbehavior data and environmental data as described above with respect toFIG. 2. At 410, the feature distribution generator module 66 determinesfeatures and distributions as described above with respect to FIG. 2. At412, the feature distribution transceiver transmits the features anddistributions and/or other parameter, location and behavior informationto the RDA server 12.

At 414, the feature reporting module 58 and/or one or more of thevehicle control modules 54, 306 may receive feedback from the RDA server12 and/or from one or more of the servers 16, 18. The feedback mayinclude as described above risk assessment scores, driving parametersand patterns, anomaly information, anomaly outliers, insurance rates,pay rates, etc.

At 416, the vehicle control modules 54, 306, 332, 334, 338 may performvarious operations based on the feedback received as described above.Some example operations are shows as operations 416A, 416B, 416C. At416A, the reporting module 338 may inform the driver of a riskassessment score and/or details regarding a driving behavior of thedriver as compared to driving behaviors of other drivers. In anotherembodiment, the reporting module 338 may also indicate inconsistentdriving behavior of the driver and/or indicate when the driver isdriving more dangerously. For example, a driver may drive differentlyfor a second trip than the driver did during a first trip, where thesame location and similar driving conditions existed for both trips. Thedriving behaviors may also be for certain times of day and/or dates ofthe year.

At 416B, the driver training module 334 may attempt to train the driveras described above based on the feedback. At 416C, the autonomouscontrol module 332 may autonomously control operation of the vehicle 50,300 based on the feedback including, for example, average drivingparameters and patterns for the location and driving conditions. Themethod may end at 418.

FIG. 9 illustrates an example risk assessment method. The method maybegin at 500. At 502, the feature distribution receiver 104 receives thefeatures and distributions and/or other parameter and behaviorinformation from the feature distribution transceiver 68. Operation 502is performed subsequent to operation 412 of FIG. 8.

At 504, the behavior description generator module 108 determines driverbehavior information as described above and may include sets of featuresand/or topics and corresponding distributions and proportions for eachdriver, trip, scene and location. As an example, at 504A, the behaviordescription generator module 108 may determine locations of targetvehicle through which that target vehicle traveled during a trip. At504B, the behavior description generator module 108 may determine driverfeatures for the locations and topics. At 504C, the behavior descriptiongenerator module 108 may implement an algorithm to divide a trip intoscenes and describe the scenes via topics. FIG. 14 shows an example ofscenes 3-6 and corresponding steering, brake, throttle, and speedbehavior information of a driver for each of the scenes at thecorresponding locations. Example topic generation is shown and describedabove with respect to FIG. 4.

At 504D, the behavior description generator module 108 may determinedriver behavioral descriptions for the topics. For example, as shown inFIG. 4, for Topic 5, the driver behavioral description is “60-70 km/hr,throttle: 5-15%” for the features of speed being approximately 63 km/hrand 70 km/hr and the throttle positions being approximately 5%, 10% and15%.

At 504E, the behavior description generator module 108 may determinetopic proportions for multiple trips for the scenes by the driver and/orother drivers. FIG. 15 shows example topic proportions for certainscenes. For the example shown, there is a 35% probability that a driverfor the location of scene 3 operates a corresponding vehicle at 60-70km/hr and/or 35% of drivers for the location of scene 3 operate acorresponding vehicle at 60-70 km/hr. For scene 3, there is a 30%probability that for the driver there is a no throttle condition (e.g.,the driver has let off a gas pedal). Examples of topic proportions fordifferent trips of an individual scene are shown in FIG. 16. The topicproportions may be for a same driver or for different drivers.

At 504F, the behavior description generator module 108 may determinefeature-portion information as described above including scenedistributions for the scenes. This may be for a particular location(e.g., location or area 505). For example, multiple trips have occurredfor the location 505. The trips have multiple corresponding topics.Trends in behavior are detected for the location, a certain time period,a certain date and/or a set of dates. Continuing from the same example,FIGS. 17-18 show example distributions collectively for thecorresponding scenes. The darker shading refers to more recent activity.A couple of trends are identified in FIG. 17 illustrating change inaverage behavior for a location. FIG. 18 shows probabilities for thefeatures associated with Topic 5, where the features with highprobabilities are the most significant features.

At 506, the driver behavior comparator module 112 (i) compares behaviordata of the driver with previous behavior data of the same driver, and(ii) compares behavior data of the driver with behavior data of otherdrivers. This is done for one or more locations. The driver behaviorcomparator module 112 may generate (i) an anomaly score for the driveras compared to other drivers for each location, and/or (ii) a singleanomaly score for the driver as compared to the other drivers for all ofthe locations.

FIG. 19 illustrates an anomaly score for a driver that exhibits similardriving behavior as other drivers. Dots are shown on the distributionsof parameter values for multiple trips of the driver. As can be seen,the dots are in regions of the distributions where most other driverparameter values are located and match the trend for the location. As aresult, the first anomaly score of 0.03 for the driver is similar toother anomaly scores of average drivers. Two anomaly scores of 0.04 fortwo other drivers are shown. Each anomaly score is provided for aparticular trip and scene and a same location. Example trip and scenenumbers are shown for the anomaly scores shown. Example topicproportions are shown for the driver corresponding to each of theanomaly scores. FIG. 19 shows that common scenes match the trend for thelocation.

FIG. 20 illustrates an anomaly score for a driver that exhibits adifferent driving behavior than other drivers. Dots are shown on thedistributions of parameter values for multiple trips of the driver. Ascan be seen, some of the dots are not in regions of the distributionswhere most other driver parameter values are located and thus do notmatch the trend for the location. As a result, the first anomaly score(e.g., 0.99) of for a first driver is high. Two anomaly scores of 0.97and 0.92 of other drivers are also provided and indicate that thedriving behaviors of these two drivers are also different than drivingbehaviors of the average driver. In one embodiment, the anomaly scoresare normalized to 1.0, such that 0.0 is a minimum score and 1.0 is amaximum score. Thus, the anomaly scores for the three drivers areconsidered outliers. Each anomaly score is provided for a particulartrip and scene and a same location. Example trip and scene numbers areshown for the anomaly scores shown. Example topic proportions are shownfor the drivers.

In one embodiment, the driver behavior comparator module 112 determinesthe anomaly scores by determining a mean distance between a targetvehicle trip data and other trip data of the same vehicle and/or othervehicles for the same location. If the anomaly score is low, then thereis a low deviation between the target vehicle trip and the other trips.On the other hand, if the anomaly score is high, then there is a highdeviation between the target vehicle trip and the other trips. Inanother embodiment, all trips for the same location and at any time ofday and/or date are compared. In yet another embodiment, trips for asame location and similar driving conditions (e.g., weather, time ofday, day of week, holiday, etc.) are compared.

At 508, the driver behavior comparator module 112 stores driver behaviorinformation and the anomaly scores for the corresponding locations andtrend information in the driving behavior trend and location database170. This may include the vehicle driver behavior information for eachlocation and for each driver.

At 510, the risk assessment module 114 calculates a risk assessmentscore. This may include calculating probability distribution functions(PDFs) and/or cumulative distribution functions (CDFs) for each ofmultiple drivers based on the anomaly scores for the drivers. Eachdriver has an anomaly trend for the locations. Each PDF, or density of acontinuous random variable (i.e. anomaly score), is a function whosevalue at any given sample (or point) in a sample space (a set ofpossible values taken by the random variable) can be interpreted asproviding a relative likelihood that the value of the random variablewould equal that sample. Each CDF is a probability that an anomaly scorewill take a value less than or equal to a certain anomaly score. ExamplePDFs are shown in FIG. 11 and example CDFs are shown in FIG. 12, wherean average of the PDFs and an average of the CDFs are identifiedrespectively by boxed curves 509, 511. The PDFs and CDFs that havevalues to the left of corresponding values of the average PDF andaverage CDF are for drivers with less dangerous driving behavior. ThePDFs and CDFs that have values to the right of corresponding values ofthe average PDF and average CDF are for drivers with more dangerousdriving behavior. The risk assessment score for a driver may be based onthe relative difference between the PDF and/or CDF and the average PDFand average CDF. The drivers with PDFs and CDFs to the right of theaverage PDF and the average CDF are identified as having high riskdriving behavior.

If a first location has many corresponding outlier anomaly scores ascompared to other locations, then the first location is identified as adangerous location. As a result, the risk assessment score may beadjusted to account for one or more locations being identified asdangerous. If a location is identified as being a dangerous locationthen the risk assessment score may be reduced to indicate that thedriver is not driving dangerously as compared to other drivers. If afirst driver has many outlier anomaly scores as compared to otherdrivers, then the first driver is identified as a bad, risky and/ordangerous driver. If the driving behavior of a driver has manycorresponding outlier anomaly scores as compared to previous drivingbehavior of the driver, then the driver is deemed to be drivingdangerously. The risk assessment score of a driver may be adjusted basedin the identification of the driver as being a bad, risk and/ordangerous driver. The risk assessment score may also be based on:whether the driver is having more outlier anomaly events than otherdrivers for the same locations and driving conditions; whether thedriver is having an isolated outlier anomaly event; and whether thedriver is having a same or similar outlier anomaly event(s) as otherdrivers.

At 512, the score transceiver 116 transmits the risk assessment score,the PDF, the CDF, the average PDF, the average CDF, the anomaly scoresand/or other behavioral information to the target vehicle, an insuranceserver, a fleet management server, a third party, an employer, a mobiledevice of the driver, etc. The method may end at 514.

FIG. 10 illustrates an example driver specific scale adjusting method.The method may begin at 600. At 602, a server, such as one of theservers 16, 18 receives the PDF, the CDF, the average PDF, the averageCDF, the anomaly scores and/or other behavioral information associatedwith a driver. At 604, the server and/or a calculation module (e.g., thecalculation module 204) of the server may determine whether the drivingbehavior is dangerous. If yes, then operation 606 may be performed andthe driver may be identified as a high risk driver. In anotherembodiment, a risk level is assigned to the driver based on the drivingbehavior information.

At 608, the server and/or the calculation module determines whether thedriving behavior of the driver warrants an adjustment in a current rate(e.g., an insurance rate, a pay rate, or other rate) and adjusts therate accordingly. Behavior-based insurance (BBI) rewards good driverbehavior. An insurance rate may be adjusted based on how the driver isperforming as compared to other drivers.

The above-described operations of FIGS. 8-10 are meant to beillustrative examples; the operations may be performed sequentially,synchronously, simultaneously, continuously, during overlapping timeperiods or in a different order depending upon the application. Also,any of the operations may not be performed or skipped depending on theimplementation and/or sequence of events.

The following FIGS. 21-22 provide additional examples that may beimplemented by the modules and systems disclosed herein. FIG. 21 showsan example top view of vehicles illustrating common lane transitioningbehavior at a particular location. In this example, there is a mergeahead for the vehicles. A common trend is for drivers to transition to aleft lane early of the merge to provide room for vehicles merging in aright lane. This is shown by common vehicle 650 transitioning atlocation 1 to the left lane and completing the transition prior to beingat location 2. By matching this behavior at this location, an autonomous(or target) vehicle 652 drives in a more natural manner.

FIG. 22 shows an example top view of vehicles illustrating common speedbehavior at particular locations. At location 1, vehicles commonly drive5 mph above a speed limit, but at location 2 vehicles drive at the speedlimit because local drivers know that the speed limit is strictlyenforced. This is shown by common vehicle 660. An autonomous (or target)vehicle 662 is safer by behaving similarly as other common vehicles andavoids a speeding ticket.

FIG. 23 shows an example display 700 reporting driving behaviorinformation. The driving behavior information may include as shown aplot 702 of the driver's safety score, a plot 704 of the driver'sinsurance cost, and/or a current insurance fee and safety score window706. Example plots are shown for a safe driver and a more dangerousdriver and designated 702A, 702B, 704A, 704B. The plots and window maybe shown on the display 700.

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure can be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Spatial and functional relationships between elements (for example,between modules, circuit elements, semiconductor layers, etc.) aredescribed using various terms, including “connected,” “engaged,”“coupled,” “adjacent,” “next to,” “on top of,” “above,” “below,” and“disposed.” Unless explicitly described as being “direct,” when arelationship between first and second elements is described in the abovedisclosure, that relationship can be a direct relationship where noother intervening elements are present between the first and secondelements, but can also be an indirect relationship where one or moreintervening elements are present (either spatially or functionally)between the first and second elements. As used herein, the phrase atleast one of A, B, and C should be construed to mean a logical (A OR BOR C), using a non-exclusive logical OR, and should not be construed tomean “at least one of A, at least one of B, and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A.

In this application, including the definitions below, the term “module”or the term “controller” may be replaced with the term “circuit.” Theterm “module” may refer to, be part of, or include: an ApplicationSpecific Integrated Circuit (ASIC); a digital, analog, or mixedanalog/digital discrete circuit; a digital, analog, or mixedanalog/digital integrated circuit; a combinational logic circuit; afield programmable gate array (FPGA); a processor circuit (shared,dedicated, or group) that executes code; a memory circuit (shared,dedicated, or group) that stores code executed by the processor circuit;other suitable hardware components that provide the describedfunctionality; or a combination of some or all of the above, such as ina system-on-chip.

The module may include one or more interface circuits. In some examples,the interface circuits may include wired or wireless interfaces that areconnected to a local area network (LAN), the Internet, a wide areanetwork (WAN), or combinations thereof. The functionality of any givenmodule of the present disclosure may be distributed among multiplemodules that are connected via interface circuits. For example, multiplemodules may allow load balancing. In a further example, a server (alsoknown as remote, or cloud) module may accomplish some functionality onbehalf of a client module.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. The term shared processor circuitencompasses a single processor circuit that executes some or all codefrom multiple modules. The term group processor circuit encompasses aprocessor circuit that, in combination with additional processorcircuits, executes some or all code from one or more modules. Referencesto multiple processor circuits encompass multiple processor circuits ondiscrete dies, multiple processor circuits on a single die, multiplecores of a single processor circuit, multiple threads of a singleprocessor circuit, or a combination of the above. The term shared memorycircuit encompasses a single memory circuit that stores some or all codefrom multiple modules. The term group memory circuit encompasses amemory circuit that, in combination with additional memories, storessome or all code from one or more modules.

The term memory circuit is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium may therefore be considered tangible and non-transitory.Non-limiting examples of a non-transitory, tangible computer-readablemedium are nonvolatile memory circuits (such as a flash memory circuit,an erasable programmable read-only memory circuit, or a mask read-onlymemory circuit), volatile memory circuits (such as a static randomaccess memory circuit or a dynamic random access memory circuit),magnetic storage media (such as an analog or digital magnetic tape or ahard disk drive), and optical storage media (such as a CD, a DVD, or aBlu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks,flowchart components, and other elements described above serve assoftware specifications, which can be translated into the computerprograms by the routine work of a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory, tangible computer-readablemedium. The computer programs may also include or rely on stored data.The computer programs may encompass a basic input/output system (BIOS)that interacts with hardware of the special purpose computer, devicedrivers that interact with particular devices of the special purposecomputer, one or more operating systems, user applications, backgroundservices, background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), or JSON (JavaScript Object Notation) (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) source code for compilationand execution by a just-in-time compiler, etc. As examples only, sourcecode may be written using syntax from languages including C, C++, C#,Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl,Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5threvision), Ada, ASP (Active Server Pages), PHP (PHP: HypertextPreprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, VisualBasic®, Lua, MATLAB, SIMULINK, and Python®.

None of the elements recited in the claims are intended to be ameans-plus-function element within the meaning of 35 U.S.C. § 112(f)unless an element is expressly recited using the phrase “means for,” orin the case of a method claim using the phrases “operation for” or “stepfor.”

1. A risk assessment system comprising: a behavior description generatormodule configured to at a risk assessment server receive a first set ofdriving features and topics corresponding to a driving behavior of afirst driver of a first vehicle in a location and under a first set ofdriving conditions; a driving behavior comparator module configured to(i) compare the first set of driving features and the topics to one ormore other sets of features and topics, wherein the one or more othersets of features and topics correspond to driving behaviors of otherdrivers for the location and same or similar driving conditions as thefirst set of driving conditions, and (ii) generate an anomaly score forthe first driver based on results of the comparison; a risk assessmentmodule configured to calculate a risk assessment score for the driverbased on the anomaly score, wherein the risk assessment score isindicative of a risk level of the first driver relative to the otherdrivers; and a transceiver configured to transmit the risk assessmentscore from the risk assessment server to at least one of a vehiclecontrol module of the first vehicle or a second server, wherein the riskassessment server and the second server are separate from the firstvehicle.
 2. The risk assessment system of claim 1, wherein the riskassessment module is configured to calculate the risk assessment scorebased on a plurality of anomaly scores of the first driver and anomalyscores of the other drivers.
 3. The risk assessment system of claim 1,wherein: the driving behavior comparator module is configured togenerate a plurality of anomaly scores for the first driver; and therisk assessment module is configured to (i) based on the plurality ofanomaly scores, determine at least one of a probability distributionfunction or a cumulative distribution function, (ii) compare the atleast one of the probability distribution function or the cumulativedistribution function to at least one of an average probabilitydistribution function or an average cumulative distribution function,and (iii) calculate the risk assessment score based on the comparisonbetween the at least one of the probability distribution function or thecumulative distribution function for the first driver to the at leastone of the average probability distribution function or the averagecumulative distribution function.
 4. The risk assessment system of claim1, wherein: the driving behavior comparator module is configured tocompare the features and the topics corresponding to the drivingbehavior of the first driver to past features and topics correspondingto past driving behaviors of the first driver, and indicate whether thefirst driver is driving more dangerously than in the past; and the riskassessment module is configured to calculate the risk assessment scorebased on the indication of whether the first driver is driving moredangerously than in the past.
 5. The risk assessment system of claim 1,wherein the risk assessment module is configured to: compare the anomalyscore for the first driver for a first trip at the location to otheranomaly scores for other trips at the location; determine whether theanomaly score for the first driver is an outlier as compared to theother anomaly scores; and based on whether the anomaly score for thefirst driver is an outlier, (i) determine a new common driving behaviorfor the first driver and the other drivers, or (ii) determine that thefirst driver is driving more dangerously than in the past.
 6. The riskassessment system of claim 1, further comprising the second server,wherein the second server is configured to, based on the risk assessmentscore, adjust an insurance rate or a pay rate of the first driver. 7.The risk assessment system of claim 1, further comprising a latentDirichlet allocation module configured to determine the topics based onprobabilities of the features.
 8. The risk assessment system of claim 1,wherein the driving behavior comparator module is configured to (i)determine topic proportions for scenes of one or more trips of the firstdriver, (ii) based on the topic proportions, determine whether thedriving behavior of the first driver matches an average or trendingdriving behavior of the other drivers, and (iii) generate the anomalyscore for the first driver based on whether the driving behavior of thefirst driver matches the average or trending driving behavior of theother drivers.
 9. The risk assessment system of claim 1, wherein thedriving behavior comparator module is configured to: divide a trip ofthe first driver into a plurality of scenes; describe the scenes via thetopics of the first set of driving features and topics; determine topicproportions for the plurality of scenes; determine feature-proportioninformation for the plurality of scenes or trends in the drivingbehavior of the first driver and driving behaviors of the other drivers;and generate at least the anomaly score for the first driver based onthe topic proportions and feature-proportion information.
 10. A riskassessment system comprising: a driving behavior comparator moduleconfigured to (i) perform comparisons between sets of features andtopics for driving behaviors of a plurality of drivers, wherein each ofthe comparisons is for a same driving location and same or similardriving conditions, and (ii) generate a plurality of anomaly scores forthe plurality of drivers based on results of the comparison; anassessment module configured to, based on the anomaly scores, determineat least one of a common driving behavior or a trend in the drivingbehaviors of the plurality of drivers for the driving locations andcorresponding ones of the driving conditions; and a transceiverconfigured to transmit information regarding the common driving behavioror the trend in the driving behaviors of the plurality of drivers to atarget vehicle for at least one of training a driver of the targetvehicle or autonomous control of the target vehicle.
 11. The riskassessment system of claim 10, wherein: the assessment module isconfigured to, based on the anomaly scores, calculate a risk assessmentscore, wherein the risk assessment score indicates whether the driver ofthe target vehicle is driving more dangerously than other ones of theplurality of drivers; and the transceiver is configured to transmit therisk assessment score to the target vehicle.
 12. The risk assessmentsystem of claim 10, wherein: the assessment module is configured to (i)determine at least one of an average probability distribution functionor an average cumulative distribution function for trips of theplurality of drivers at the driving locations, and (ii) based on the atleast one of the average probability distribution function or theaverage cumulative distribution function, calculate a risk assessmentscore for the driver of the target vehicle; and the transceiver isconfigured to transmit the risk assessment score to the target vehicle.13. The risk assessment system of claim 10, wherein the informationregarding the at least one of the common driving behavior or the trendin the driving behaviors of the plurality of drivers includes at leastone of (i) average features of the driving behaviors of the plurality ofdrivers in the driving locations, (ii) driving patterns for theplurality of drivers driving in the driving locations, or (iii) trendsin the driving behaviors of the plurality of drivers driving in thedriving locations.
 14. The risk assessment system of claim 10, wherein:the driving behavior comparator module is configured to obtaininformation indicative of a driving behavior of the driver of the targetvehicle; and the assessment module are configured to generate a riskassessment score for the driver of the target vehicle based on (i) theinformation indicative of the driving behavior of the driver of thetarget vehicle, (ii) the common driving behavior, and (iii) the trend inthe driving behaviors of the plurality of drivers; and the transceiveris configured to transmit the risk assessment score to the targetvehicle.
 15. A system for a first vehicle, the system comprising: adisplay; a plurality of sensors and a global positioning systemconfigured to determine a first plurality of parameters and drivingconditions for a location of the first vehicle, wherein the firstplurality of parameters indicate at least a current or upcoming locationof the first vehicle; a transceiver configured to transmit from thefirst vehicle the first plurality of parameters and driving conditionsto a risk assessment server, wherein the risk assessment server isseparate from the vehicle; wherein the transceiver is configured to,based on the first plurality of parameters and driving conditions,receive information regarding a common driving behavior for a pluralityof drivers, wherein the plurality of drivers do not include a firstdriver of the first vehicle, and wherein the common driving behavior isindicative of an average driving behavior of the plurality of driversfor the current or upcoming location; at least one vehicle controlmodule configured to, based on the information regarding the commondriving behavior, at least one of (i) train the first driver of thefirst vehicle, or (ii) autonomously control operation of the firstvehicle; and a feature reporting module configured to, subsequent to theat least one of (i) training the first driver, or (ii) autonomouslycontrolling operation of the first vehicle, determine features andfeature distributions based on a second plurality of parameters, whereinthe transceiver is configured to (i) transmit at least one of thefeatures or the feature distributions to the risk assessment server,(ii) receive a risk assessment score for a driver of the vehicle basedon the at least one of the features or the feature distributions, and(iii) receive at least one of an insurance rate or a pay rate, the atleast one of the insurance rate or the pay rate are generated based onthe risk assessment score, and the at least one vehicle control moduleis configured to display the risk assessment score and the at least oneof the insurance rate or the pay rate on the display.
 16. The system ofclaim 15, wherein the at least one vehicle control module is configuredto, based on the information regarding the common driving behavior,display indicators on the display to train the driver of the firstvehicle to have a driving behavior more like the average drivingbehavior of the plurality of drivers.
 17. The system of claim 15,wherein the at least one vehicle control module is configured to, basedon the information regarding the common driving behavior, autonomouslycontrol operation of the first vehicle to match the average drivingbehavior of the plurality of drivers.
 18. (canceled)
 19. The system ofclaim 15, wherein the at least one vehicle control module displays theinsurance rate or the pay rate on the display to indicate to the firstdriver a current driving behavior of the first driver as an indicatorthat the first driver is driving safe or as an incentive to driversafer.
 20. The risk assessment system of claim 1, further comprising afeature-portion information generator module configured to determine adriving behavior trend based on driving behavior descriptions, wherein:the behavior description generator module is configured to (i) extractfrom feature-quantity distributions feature quantities specific to eachof the topics using a likelihood ratio to generate the driving behaviordescriptions; and the driving behavior comparator module is configuredto generate the anomaly score based on the driving behavior trend. 21.The risk assessment system of claim 20, wherein the behavior descriptiongenerator module is configured to calculate the likelihood ratio basedon: feature quantities corresponding to a particular bin; apredetermined one of the topics; a multinomial distribution indicativeof a base feature-quantity distribution of the predetermined one of thetopics; a conditional probability of the feature quantities given amultinomial distribution of the predetermined one of the topics; and asum of conditional probabilities obtained for the topics.
 22. The riskassessment system of claim 1, wherein the one or more other sets offeatures and topics correspond to the driving behaviors of the otherdrivers of vehicles other than the first vehicle for the location andthe same or similar driving conditions as the first set of drivingconditions.
 23. The system of claim 15, wherein the at least one vehiclecontrol module is configured to display the risk assessment score andthe at least one of the insurance rate or the pay rate on the displaywhile the first vehicle is being driving by the first driver.